Insights Into Remote Site Conditions Without Leaving Your Desk

Gain Insights Into Remote Site Conditions Without Leaving Your Desk: Industry trends, tips, and practical uses for aerial imagery and geographic data from First Base Solutions, the experts who brought you MapWarehouse.

Monday, July 28, 2014

Mapping the Fourth Dimension

Chapter Review: Tracking Animals in a Dynamic Environment:

Remote Sensing Image Time Series

Prominent theoretical physicist Stephen Hawking tells us that time is a property of matter, just like colour, mass, or temperature; without matter, time does not exist. Doc Brown in the Back To The Future movie trilogy tells us we must think fourth dimensionally in order to travel through time safely. The reality is, everyone and everything is traveling through time, all day, every day, at the minuscule but consistent rate of one second per second. Unfortunately for us in the mapping biz, this time travel stuff really complicates things since our traditional paper maps are only 2-dimensional and digital mapping is 3-dimensional at best. Since the beginning of, er… well, time, incorporating temporal information into spatial models has been challenging and is often outright excluded from consideration. Since time affects so much of the world around us, it just makes good sense to give the matter the time and attention it deserves.

Let’s break out the flux capacitor for just a moment and look into the past. Paper maps get updated and republished from time to time, digital mapping databases get updated on regular or random intervals, in whole or in part. While we usually keep track of when edits are introduced in our digital mapping systems and state the copyright date on paper editions, time marches on and details of dynamic systems in the landscape become out of date rather quickly. As Canadians, we see this as the seasons change, but does anyone replace their map collections four times per year? Our mapping often includes static hydrology information showing the spatial location of water bodies, for example, but is limited in that it gives no temporal context to show how those water bodies change over time with Spring flooding or tide cycles.So, how do we solve this conundrum in the timeliest way possible? Picture how stop motion animation or time lapse photography works. Many individual image frames are viewed rapidly in an equal-interval, time-based sequence, giving the impression of movement created from many, many static snapshots, each showing the position of the subject at each particular moment. Now, take this concept and imagine the image frames are aerial or satellite imagery. You’ve probably seen something like this in your local weather forecast using satellite mounted radar to track storms, each frame in the video separated in time by a few minutes or hours, with a static map in the background. In our modern times, we can create our own image time series maps using time stamped imagery from commercial satellites. The contributors in Spatial Database for GPS Wildlife Tracking Data have done just that as part of their research, in this case, tracking GPS tagged roe deer in northern Italy as they respond to seasonal changes in their environment by relocating to the more productive areas of the habitat available to them. Modelling animal movement assumes these environmental factors are important to explain the “where” questions that determine the boundaries of the herd’s territory, but traditional modelling has relied on static maps comprised of roads, water, land cover types; features that don’t change up too often. Seasonal biomass accumulation and daily traffic patterns, for example, reveal a far more complex and dynamic view of what’s actually happening on the ground. Incorporating a detailed temporal element into the model is how to answer the “why” and “when” questions.For this study, the satellite of choice was MODIS (Moderate-resolution Imaging Spectroradiometer - MODIS produces course imagery, 250-1000m pixels, best suited to observing very large scale phenomena such as weather systems, wildfires, and ocean conditions) and the temporal variable was NDVI (Normalized Difference Vegetation Index - a common image processing technique to filter out the background brightness of the soil which allows better interpretation of the vegetation health and productivity). Each image and therefore each pixel in the time series was reprojected, mosaicked and further processed, and ultimately assigned a time range, in this case in intervals of 16 days, that can be queried and intersected with any other georeferenced features and time-stamped data such as feedback from the GPS tagged roe deer. It’s easy to see where the deer are within their habitat, when they’re on the go, and what the NDVI and ground conditions were like at the time of migration.Now, unless you’re an avid hunter, deer movement may not interest you so much, but there’s a lot more can be done with time series technology. An area of great potential is precision agriculture since the same indicators that measure forest productivity can be used to estimate crop yields through the growing season. Even studying just a small area, the results can be used to predict outcomes for nearby fields with similar conditions. Burning questions about global warming and change detection - ocean surface temperatures, snow pack depth, iceberg flows, desert encroachment and other changes in land use patterns can all be clarified with an emphasis on mapping in the fourth dimension. Human activity as well can be plugged into the model. We may not be GPS tagged like the roe deer, but enough of us carry a smartphone with Google Maps for mobile enabled that we can see a pretty reliable view of traffic congestion in real time and make detours based on that information that other driver’s will see, and in turn, react to the change in conditions. Even predictive modelling in socioeconomic terms can benefit from time series imagery. Some variables derived from satellite imagery typically get used to enhance demographic data, such as using average building footprint size intersected with population density to predict median income at the neighbourhood level with surprising accuracy. While these models have been used in the past to predict or infer median income in places where only building footprint is available without the availability of intersecting population data, adding a temporal dimension can be used to spot urban trends that occur over a matter of years. I feel like I’m in school again, preparing a book report and all, but the best practices and methodology described in detail here are definitely worth a look. Of course, if you’ve got a bald spot from scratching your head after reading this chapter, FBS has the tools and know-how to help you set up a custom mapping solution. Time’s a wasting, so give us a call today.